# Copyright 2020 The HuggingFace Evaluate Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" seqeval metric. """

import importlib
from typing import List, Optional, Union

import datasets
import evaluate
from seqeval.metrics import accuracy_score, classification_report


_CITATION = """\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

_DESCRIPTION = """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

_KWARGS_DESCRIPTION = """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = evaluate.load("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Seqeval(evaluate.Metric):
	def _info(self):
		return evaluate.MetricInfo(
			description = _DESCRIPTION,
			citation = _CITATION,
			homepage = "https://github.com/chakki-works/seqeval",
			inputs_description = _KWARGS_DESCRIPTION,
			features = datasets.Features(
				{
					"predictions": datasets.Sequence(datasets.Value("string", id = "label"), id = "sequence"),
					"references": datasets.Sequence(datasets.Value("string", id = "label"), id = "sequence"),
				}
			),
			codebase_urls = ["https://github.com/chakki-works/seqeval"],
			reference_urls = ["https://github.com/chakki-works/seqeval"],
		)

	def _compute(
		self,
		predictions,
		references,
		suffix: bool = False,
		scheme: Optional[str] = None,
		mode: Optional[str] = None,
		sample_weight: Optional[List[int]] = None,
		zero_division: Union[str, int] = "warn",
	):
		if scheme is not None:
			try:
				scheme_module = importlib.import_module("seqeval.scheme")
				scheme = getattr(scheme_module, scheme)
			except AttributeError:
				raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")
		report = classification_report(
			y_true = references,
			y_pred = predictions,
			suffix = suffix,
			output_dict = True,
			scheme = scheme,
			mode = mode,
			sample_weight = sample_weight,
			zero_division = zero_division,
		)
		report.pop("macro avg")
		report.pop("weighted avg")
		overall_score = report.pop("micro avg")

		scores = {
			type_name: {
				"precision": score["precision"],
				"recall": score["recall"],
				"f1": score["f1-score"],
				"number": score["support"],
			}
			for type_name, score in report.items()
		}
		scores["overall_precision"] = overall_score["precision"]
		scores["overall_recall"] = overall_score["recall"]
		scores["overall_f1"] = overall_score["f1-score"]
		scores["overall_accuracy"] = accuracy_score(y_true = references, y_pred = predictions)

		return scores